Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization
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Abstract (arXiv)
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiveness of IoT applications needs to face the limitation of available resources, including spectrum, energy, computing, learning and inference capabilities. This paper challenges the prevailing approach to IoT communication, which prioritizes the usage of resources in order to guarantee perfect recovery, at the bit level, of the data transmitted by the sensors to the central unit. We propose a novel approach, called goal-oriented (GO) IoT system design, that transcends traditional bit-related metrics and focuses directly on the fulfillment of the goal motivating the exchange of data. The improvement is then achieved through a comprehensive system optimization, integrating sensing, communication, computation, learning, and control. We provide numerical results demonstrating the practical applications of our methodology in compelling use cases such as edge inference, cooperative sensing, and federated learning. These examples highlight the effectiveness and real-world implications of our proposed approach, with the potential to revolutionize IoT systems.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2023.
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Citing papers per year: 2024: 3; 2025: 7; 2026: 2.
12 papers.
- 2025Adaptive Semantic Token Communication for Transformer-based Edge Inferencecited by 14
- 2024Dynamic Relative Representations for Goal-Oriented Semantic Communicationscited by 11
- 2025Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networkscited by 7
- 2025"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systemscited by 7
- 2024Semantic Communication Enhanced by Knowledge Graph Representation Learningcited by 5
- 2025Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combiningcited by 5
- 2025Latent Space Alignment for AI-Native MIMO Semantic Communicationscited by 5
- 2025Learning Network Sheaves for AI-native Semantic Communicationcited by 4
- 2025Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantizationcited by 3
- 2024Goal-oriented Communications based on Recursive Early Exit Neural Networkscited by 1
- 2026Federated Latent Space Alignment for Multi-user Semantic Communicationscited by 1
- 2026Information Bottleneck Meets Quantization: Finite Rate Analysis and Optimal Designscited by 0
- 2026Information Bottleneck Meets Quantization: Finite Rate Analysis and Optimal Designs9 Jun 2026
- 2026Federated Latent Space Alignment for Multi-user Semantic Communications19 Feb 2026
- 2025Learning Network Sheaves for AI-native Semantic Communication2 Dec 2025
- 2025"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems25 Jul 2025
- 2025Latent Space Alignment for AI-Native MIMO Semantic Communications22 Jul 2025
- 2025Adaptive Semantic Token Communication for Transformer-based Edge Inference23 May 2025
- 2025Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining17 Apr 2025
- 2025Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks31 Mar 2025
- 2025Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization5 Jan 2025
- 2024Goal-oriented Communications based on Recursive Early Exit Neural Networks27 Dec 2024
- 2024Semantic Communication Enhanced by Knowledge Graph Representation Learning27 Jul 2024
- 2024Dynamic Relative Representations for Goal-Oriented Semantic Communications25 Mar 2024